Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use

Many water resources optimization problems involve conflicting objectives which the main goal is to find a set of optimal solutions on, or near to, Pareto front. In this study a multi-objective water allocation model was developed for optimization of conjunctive use of surface water and groundwater resources to achieve sustainable supply of agricultural water. Here, the water resource allocation model is based on simulation-optimization (SO) modeling approach. Two surrogate models, namely an Artificial Neural Network model for groundwater level simulation and a Genetic Programming model for TDS concentration prediction were coupled with NSGA-II. The objective functions involved: 1) minimizing water shortage relative to the water demand, 2) minimizing the drawdown of groundwater level, and 3) minimizing the groundwater quality changes. According to the MSE and R² criteria, the results showed that the surrogate models for prediction of groundwater level and TDS concentration performed favorably in comparison to the measured values at the number of observation wells. In Najaf Abad plain case study, the average drawdown was limited to 0.18 m and the average TDS concentration also decreased from 1257 mg/lit to 1229 mg/lit under optimal conditions.     

Conjunctive Management of Large-Scale Pressurized Water Distribution and Groundwater Systems in Semi-Arid Area with Parallel Genetic Algorithm

This study develops a production well management model for the conjunctive management of water resources in semi-arid areas. The management model integrates a large-scale pressurized water distribution system and a three-dimensional groundwater model under an optimization framework. The well pump operations optimization problem is formulated as a Boolean integer nonlinear programming (BINLP) problem to optimize the periodic 24-h pump on/off operations over a 1-week operation horizon. The management model considers multiple objectives and is solved by a parallel genetic algorithm (PGA) to overcome the difficulty of solving the BINLP problem. The PGA significantly reduces computation time for a case study in Chandler, Arizona. The Chandler water distribution model is built based on EPANET, and the Chandler three-dimension groundwater model is developed using MODFLOW. The high performance computing (HPC) of the genetic algorithm makes it possible to obtain 24-h real-time operations in the 7-day forecast model. The tank reliability, resilience, and vulnerability (R-R-V) are evaluated to infer the system reliability. The Pareto curve provides compromise solutions between the two competing objectives of energy reduction and pressure violation reduction.     

Solving Hydropower Unit Commitment Problem Using a Novel Sequential Mixed Integer Linear Programming Approach

Hydro Unit Commitment (HUC) is an important problem of power systems and when it is dealt with via a mathematical programming approach and optimization, it leads to the complicated class of mixed-integer nonlinear programming (MINLP). Many attempts have been made to solve the problem efficiently, while there is still ongoing research to come up with better solution schemes in terms of runtime and optimality. Highly nonlinear nature of the relationships and constraints in the optimization problem have forced the researchers to deal with the HUC problem in simplified manners which may result in impractical and unreliable solutions, i.e. schedules. Here in this paper we proposed a new method based on sequential mixed-integer linear programming (MILP) for solving a more realistic version of the HUC problem efficiently. We applied the proposed method to a cascade of two hydropower plants, Karun-3 and Karun-4, located in the Southwest of Iran. The sequential MILP approach was compared with several MINLP solvers of the GAMS optimization package. The results indicated that the proposed methodology outperformed the MINLP solvers in terms of efficiency, with solution time of less than 30 s, compared to 10 min that were given to the solvers, and in terms of optimality with more than 20 thousand cubic meters per day in water release. Additionally, we have explored the effect of penalizing the total number of startups on the total release, convergence of the algorithm, and the computation time. In all of the cases the total number of startups was reduced more than three times.

     

Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management

Determining the optimal rates of groundwater extraction for the sustainable use of coastal aquifers is a complex water resources management problem. It necessitates the application of a 3D simulation model for coupled flow and transport simulation together with an optimization algorithm in a linked simulation-optimization framework. The use of numerical models for aquifer simulation within optimization models is constrained by the huge computational burden involved. Approximation surrogates are widely used to replace the numerical simulation model, the widely used surrogate model being Artificial Neural Networks (ANN). This study evaluates genetic programming (GP) as a potential surrogate modeling tool and compares the advantages and disadvantages with the neural network based surrogate modeling approach. Two linked simulation optimization models based on ANN and GP surrogate models are developed to determine the optimal groundwater extraction rates for an illustrative coastal aquifer. The surrogate models are linked to a genetic algorithm for optimization. The optimal solutions obtained using the two approaches are compared and the advantages of GP over the ANN surrogates evaluated.     

Multi Objective Simulation-Optimization Approach in Pollution Spill Response Management Model in Reservoirs

In this study, a Pollution Spill Response Management Model (PSRMM) is developed to provide an emergency response on reservoir operation during accidental injection of hazardous material to reservoirs. PSRMM consist of spatial system analyzing (SSA) model, 2D hydrodynamic and water quality simulation model (CE-QUAL-W2), and multi-objective particle swarm optimization (MOPSO) algorithm. CE-QUAL-W2 model is applied for spatial and temporal analysis of water body in simulation routine of PSRMM. Also, in an advanced modeling framework, CE-QUAL-W2 is coupled with MOPSO algorithm to obtain desirable near optimal reservoir operation strategy and/or emergency planning in selective withdrawal framework. The simulation-optimization (SO) routine of PSRMM provides pareto optimum reservoir operation strategy in selective scheme to minimize reservoir cleanup time and to reduce the magnitude and frequency of water quality standard violations. The proposed tool is applied in Ilam reservoir in Iran, as a multipurpose hydraulic project providing water for drinking, irrigation, and flood control during an accident spill of conservative hazardous material. Different scenarios are defined and tested employing the proposed PSRMM for managing accidental spill of conservative pollutant into the reservoir.     

寺坪水电站优化调度模型

随着社会经济的快速发展,水资源供需矛盾日趋尖锐,如何合理调度有限的水资源已成为水资源管理中的现实而紧迫的任务。通过进一步研究水库调度的机理,采用非线性规划作为优化求解方法构建了水库优化调度模型,非线性规划结合了线性规划和动态规划各自的优点,并将水库调度中的各种因素融入数学模型中,较准确地计算模拟期间各种优化变量数值条件下的目标函数值,并比较得出最优值。之后,基于多目标思想,给出了一组Pareto前沿解集,通过寺坪水电站的应用,构建不同的目标函数,得到一系列决策方案,以便决策者选择偏好的决策方案。     

Risk-Cost Optimization of Hydraulic Structures: Methodology and Case Study

Simultaneous consideration of initial construction cost and failure risk in practical design of critical hydraulic structures is a long-standing problem in water resources planning and management. Despite extensive efforts to effectively tackle this problem during recent decades, the traditional rather inefficient technique of return period approach still enjoys being the dominant technique in real-world projects. Accordingly, this article aims at developing an innovative evolutionary-computation-based multi-objective model as a remedy to shortcomings of existing common methods. This simulation-optimization framework generates a series of uniformly distributed trade-off solutions which represent the compromise between competing objectives of construction cost and failure risk. Hydrologic and hydraulic uncertainties along with flood routing process are considered in the optimization process to provide a more complete picture of the problem. This approach is demonstrated and discussed for flood diversion system of Bakhtiari Dam in Iran and the Pareto optimal fronts are found for different combinations of uncertainties. The presented multi-objective approach can effectively enlarge the decision maker’s scope to more efficiently determine the optimum design of the system.     

Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty

Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximizing the total pumping rate, minimizing the total injection rate, and minimizing the solute mass increase are considered in the optimization model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is utilized to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimization model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.

     

Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints

Management of water resources has become more complex in recent years as a result of changing attitudes towards sustainability and the attribution of greater attention to environmental issues, especially under a scenario of water scarcity risk introduced by climate changes and anthropogenic pressures. This study addresses the optimal short-term operation of a multi-purpose hydropower system under an environment where objectives are conflicting. New optimization models using mixed integer nonlinear programming (MINLP) with binary variables adopted for incorporating unit commitment constraints and adaptive real-time operations are developed and applied to a real life hydropower reservoir in Brazil, utilizing evolutionary algorithms. These formulations address water quality concerns downstream of the reservoir and optimal operations for power generation in an integrated manner and deal with uncertain future flows due to climate change. Results obtained using genetic algorithm (GA) solvers were superior to gradient based methods, converging to superior optimal solutions especially due to computational intractability problems associated with combinatorial domain of integer variables in the unit commitment formulation. The adaptive operation formulation in conjunction with the solution of turbine unit commitment problem yielded more reliable solutions, reducing forecasting uncertainty and providing more flexible operational rules.     

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.     

Interval-parameter Two-stage Stochastic Semi-infinite Programming: Application to Water Resources Management under Uncertainty

In this study, an interval-parameter two-stage stochastic semi-infinite programming (ITSSP) method was developed for water resources management under uncertainty. As a new extension of mathematical programming methods, the developed ITSSP approach has advantages in uncertainty reflection and policy analysis. In order to better account for uncertainties, the ITSSP approach is expressed with discrete intervals, functional intervals and probability density functions. The ITSSP method integrates the two-stage stochastic programming (TSP), interval programming (IP) and semi-infinite programming (SIP) within a general optimization framework. The ITSSP has an infinite number of constraints because it uses functional intervals with time (t) being an independent variable. The different t values within the range [0, 90] lead to different constraints. At same time, ITSSP also includes probability distribution information. The ITSSP method can incorporate pre-defined water resource management policies directly into its optimization process to analyze various policy scenarios having different economic penalties when the promised amounts are not delivered. The model is applied to a water resource management system with three users and four periods (corresponding to winter, spring, summer and fall, respectively). Solutions of the ITSSP model provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable interval solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flow. The obtained solutions are useful for decision makers to obtain insight regarding the tradeoffs between environmental, economic and system reliability criteria.     

Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms

Combined simulation-optimization models have been widely used to address the management of water resources issues. This paper presents a simulation-optimization model for conjunctive use of surface water and groundwater at a basin-wide scale, the Zayandehrood river basin in west central Iran. In the Zayandehrood basin, in the past 10 years, a historical low rainfall in the head of the basin, combined with growing demand for water, has triggered great changes in water management at basin and irrigation system level. The conjunctive use model that coupled numerical simulation with nonlinear optimization is used to minimize shortages of water in meeting irrigation demands for four irrigation systems. Constraints guarantee the maximum/minimum cumulative groundwater drawdown and maximum capacity of irrigation systems. A support vector machines (SVMs) model is developed as a simulator of surface water and groundwater interaction model while a genetic algorithm (GA) is used as the optimization model. Conjunctive use model runs for three scenarios. Results show that the accuracy of SVMs as a simulator for surface water and groundwater interaction model is good and that it is possible to decrease the water shortage for irrigation systems with application of proposed SVMs-GA model.     

Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach

To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.

     

基于遗传算法的灰色非线性水环境管理模型及应用

以郑州市贾鲁河水环境治理规划为基础,统筹考虑到治理区域内的工程建设、水环境改善策略、污水处理厂建设运行以及配套水库调度等,构建了灰色非线性水环境管理模型,并选择遗传算法作为计算手段,对该模型进行了求解。构建的模型同时考虑到了治理区域内的污水处理厂优化运行、南水北调补水、区域内3个生态湖泊与治理河段的净化能力等。通过优化运行协调,实现了运行费用经济合理、水生态环境改善、水质达标等多项治理目标。研究结果表明,基于遗传算法的灰色非线性规划在区域水环境治理中的应用潜力十分显著。     

Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks

In last two decades, multiobjective evolutionary algorithms (MOEAs) have shown their merit for solving different optimization problems within the context of water resources and environmental engineering. MOEAs mainly use the concept of Pareto dominance for obtaining the trade-off solutions considering different criteria. A new alternative method for solving multiobjective problems is multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses scalarizing the objective functions. In this paper, decomposition strategies are developed for the large-scale water distribution network (WDN) design problems by integrating the concepts of harmony search (HS) and genetic algorithm (GA) within the MOEA/D framework. The proposed algorithms are then compared with two well-known non-dominance based MOEAs: NSGA2 and SPEA2 across four different WDN design problems. Experimental results show that MOEA/D outperform the Pareto dominance methods in terms of both non-domination and diversity criteria. MOEA/D-HS in particular could provide very high quality solutions with a uniform distribution along the Pareto front preserving the diversity and dominating the solutions of the other algorithms. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated large-scale WDN design problems.     

Model for Optimal Operation of Water Distribution Pumps with Uncertain Demand Patterns

An optimization model is presented for pump operation based upon minimizing operation costs and indirectly the maintenance costs of pumps considering uncertainty of specified demand (load) curves. The purpose of this model is to determine pump operation to meet the uncertain demands as well as to satisfy the pressure requirements in the water distribution system. In addition, constraints on the number of pump (‘on-off’) switches are included as a surrogate to indirectly minimizing the maintenance costs. This model is a mixed integer nonlinear programming (MINLP) problem using a chance constraint formulation of the uncertain demand constraint. The optimization model was solved using the LocalSolver option in A Mathematical Programming Language (AMPL). The model was first applied to the operation of an example pumping system for an urban water distribution system (WDS) illustrating a reduction in operation costs using the optimization model. The optimization model with the chance-constraint for demand was applied for a range of demand satisfaction uncertainties. A decrease in the operation costs was observed with an increased uncertainty in demand satisfaction, which shows that the model further optimizes the operations considering the relaxed constraints. Model application could be extended to operations of pumping systems during emergencies and contingencies such as droughts, component failures etc.     

A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach

Coupled simulation-optimization models are useful tools for solving optimum water allocation and crop planning problems. In this study, the optimum crops pattern in the Arayez plain in the Karkheh river basin in Iran is determined by integration of a network flow programming (NFP) based simulation model and the shuffle frog leaping optimization algorithm (SFLA) in the form of a simulation-optimization approach. MODSIM applies NFP for finding water allocations which by use of its customization ability, the benefit of water supply for the agricultural crops is calculated based on the agronomic equations. The objective function is to maximize the total net benefit gained from crops production where the decision variables which are the irrigation depths and the cultivation areas are optimized by SFLA. Results show that by use of the coupled SFLA-NFP model, the net benefit increases 12% comparing the present situation in the plain. Also, the sensitivity analyses on effective parameters indicate that the potential maximum yield and the net price of the crops yield in the market have a direct impact on the crops optimum cultivation area.     

Heuristic Optimization Model for the Optimal Layout and Pipe Design of Sewer Systems

The design of urban stormwater systems and sanitary sewer systems consists of solving two problems: generating a layout of the system and the pipe design which includes the crown elevations, slopes and commercial pipe sizes. A heuristic model for determining the optimal (minimum cost) layout and pipe design of a storm sewer network is presented. The hierarchical procedure combines a sewer layout model formulated as a mixed-integer nonlinear programming (MINLP) problem which is solved using the General Algebraic Modeling System (GAMS) and a simulated annealing optimization procedure for the pipe design of a generated layout was developed in Excel. The GAMS and simulated annealing models are interfaced through linkage of Excel and GAMS. The pipe design model is based upon the simulated annealing method to optimize the crown elevations and diameter of pipe segments in a storm sewer network using layouts generated using GAMS. A sample scenario demonstrates that using these methods may allow for significant costs saving while simultaneously reducing the time typically required to design and compare multiple storm sewer networks.     

调水背景下丹江口水库优化调度与效益分析

针对南水北调中线一期工程建成调水这一新常态下丹江口水库调度问题,从丹江口水库蓄水、供水、发电等不同运行任务出发,通过设定弃水最小化、发电量最大化调度目标函数和不同供水调度情景,建立了丹江口水库优化调度模型,构建满足丹江口水库调度运行方式的求解空间,采用1956—2015年丹江口天然入库径流系列,运用较成熟的动态规划优化算法对丹江口优化调度进行模拟,研究水源地枢纽丹江口水库不同调度目标函数和运行方式对下游区域水文情势和供需水平衡的影响,试图为提出可权衡汉江流域多方用水需求的丹江口调度运行方式提供基础支撑。结果表明:考虑南水北调中线一期工程引水后,采用丹江口优化调度方案的下泄水量>272亿m³,能够满足下游基础供水需求。     

跨流域供水水库群联合调度规则研究

针对跨流域供水水库群联合调度存在的主从递阶结构,提出了调水规则和供水规则相结合的跨流域供水水库群联合调度规则。其中,调水规则由一组基于各水库蓄水量的调水控制线表示,根据其间的相对位置关系,决定是否调水,调水量如何分配等;供水规则由各库供水调度图表示,对应于不同用水户的限制供水线将水库的兴利库容分为若干调度区。建立了适合于主从递阶结构的水库群联合调度二层规划模型,采用并行种群混合进化的粒子群算法对模型进行求解。中国北方某大型跨流域调水工程的实例研究证明了模型的合理性和有效性。